Relaxing the assumption of having a labeled target dataset, so when there is no class label information in the target domain, some works tried to imitate the ability of humans to identify unseen classes given only a high-level description. For example, the authors in [1]} introduced high-level semantic attributes per class, and Gebru et al. [2]} leveraged these attributes to improve performance in the da of fine-grained recognition. Occasionally, when fine-tuning the network in the setting of unsupervised da, it is possible to preliminarily obtain a label of the target data, which is called a pseudo label, based on the maximum a posteriori probability. For example, Yan et al. [3]}, after the initialization of the target model exploiting the source data, defined the class a posteriori probability \(p(y^{t}_{j} = c|x^{t}_{j})\)  using the output of the target model and assigning pseudo-label \(\hat{y^{t}_{j}}\)  to \(x^{t}_{j}\)  by \(\hat{y^{t}_{j}} = \operatornamewithlimits{arg\,max}_c p(y^{t}_{j} = c|x^{t}_{j})\) .
